! git log -1 --format="%H"

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 6
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 12833, C = 7875

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 4100 | is_connectable
  205 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  246 | is_connected_step
 8405 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 600.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 600.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 7875 rows, 12833 columns and 40303 nonzeros
Model fingerprint: 0x4adcdf90
Model has 6 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 10373 integer (10373 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 99 rows and 4366 columns
Presolve time: 0.14s
Presolved: 7776 rows, 8467 columns, 40168 nonzeros
Variable types: 2460 continuous, 6007 integer (6007 binary)
Found heuristic solution: objective 128227.28873

Root relaxation: objective 2.354143e+03, 4763 iterations, 0.17 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 2354.14349    0  175 128227.289 2354.14349  98.2%     -    0s
H    0     0                    100960.17139 2354.14349  97.7%     -    0s
     0     0 4053.97421    0  275 100960.171 4053.97421  96.0%     -    0s
H    0     0                    30623.011882 4053.97421  86.8%     -    1s
     0     0 4418.33722    0  334 30623.0119 4418.33722  85.6%     -    1s
     0     0 4418.47503    0  331 30623.0119 4418.47503  85.6%     -    1s
     0     0 4418.51187    0  361 30623.0119 4418.51187  85.6%     -    1s
     0     0 4499.87977    0  391 30623.0119 4499.87977  85.3%     -    1s
H    0     0                    22776.355164 4499.87977  80.2%     -    1s
     0     0 4500.81515    0  342 22776.3552 4500.81515  80.2%     -    1s
     0     0 4500.81515    0  344 22776.3552 4500.81515  80.2%     -    1s
     0     0 4504.43885    0  405 22776.3552 4504.43885  80.2%     -    1s
H    0     0                    21758.497527 4504.43885  79.3%     -    1s
     0     0 4511.66543    0  356 21758.4975 4511.66543  79.3%     -    1s
H    0     0                    18827.019202 4511.66543  76.0%     -    1s
     0     0 4513.00320    0  355 18827.0192 4513.00320  76.0%     -    1s
H    0     0                    18768.544057 4513.00320  76.0%     -    1s
     0     0 4513.03559    0  357 18768.5441 4513.03559  76.0%     -    1s
H    0     0                    18721.583981 4513.03559  75.9%     -    1s
     0     0 4519.06648    0  336 18721.5840 4519.06648  75.9%     -    1s
H    0     0                    18667.481430 4519.06648  75.8%     -    2s
     0     0 4527.66139    0  340 18667.4814 4527.66139  75.7%     -    2s
     0     0 4527.70498    0  336 18667.4814 4527.70498  75.7%     -    2s
H    0     0                    18583.209259 4527.70498  75.6%     -    2s
     0     0 4529.80460    0  448 18583.2093 4529.80460  75.6%     -    2s
     0     0 4529.80460    0  411 18583.2093 4529.80460  75.6%     -    2s
     0     0 4530.70221    0  359 18583.2093 4530.70221  75.6%     -    2s
     0     0 4531.74381    0  348 18583.2093 4531.74381  75.6%     -    2s
     0     0 4531.98550    0  348 18583.2093 4531.98550  75.6%     -    2s
H    0     0                    18419.786870 4531.98550  75.4%     -    2s
H    0     0                    18389.786870 4531.98550  75.4%     -    2s
     0     0 4532.03971    0  380 18389.7869 4532.03971  75.4%     -    2s
     0     0 4532.13200    0  387 18389.7869 4532.13200  75.4%     -    2s
     0     0 4532.35006    0  432 18389.7869 4532.35006  75.4%     -    2s
     0     0 4532.35006    0  287 18389.7869 4532.35006  75.4%     -    3s
H    0     0                    18308.224113 4532.35006  75.2%     -    3s
H    0     2                    17896.244101 4532.35006  74.7%     -    3s
     0     2 4532.35006    0  281 17896.2441 4532.35006  74.7%     -    3s
H   31    40                    17871.860204 4819.61574  73.0%   711    4s
H   32    40                    17869.696070 4819.61574  73.0%   700    4s
H   33    40                    17864.053355 4819.61574  73.0%   682    4s
    74    83 5958.17545    8  242 17864.0534 4819.61574  73.0%   437    5s
H  301   297                    17646.627759 4819.61574  72.7%   202    7s
H  324   322                    16992.028979 4819.61574  71.6%   197    7s
H  384   389                    16956.245458 4819.61574  71.6%   183    7s
  1163  1140 6487.41600    8  314 16956.2455 5101.30161  69.9%   131   10s
  1449  1288 12783.6056  129  181 16956.2455 5133.62810  69.7%   132   15s
  1462  1297 6750.03049   53  315 16956.2455 5133.62810  69.7%   131   22s
  1465  1302 5133.62810   14  367 16956.2455 5133.62810  69.7%   157   26s
  1478  1311 5133.62810   17  313 16956.2455 5133.62810  69.7%   161   30s
  1539  1346 6050.22823   20  268 16956.2455 5133.62810  69.7%   173   35s
H 1544  1279                    16956.245448 5133.62810  69.7%   173   35s
H 1545  1216                    16956.245437 5133.62810  69.7%   173   35s
H 1546  1158                    16956.245435 5133.62810  69.7%   173   35s
H 1581  1120                    16456.992491 5133.62810  68.8%   178   37s
H 1617  1081                    16439.212418 5133.62810  68.8%   178   38s
H 1666  1040                    16403.524303 5133.62810  68.7%   182   39s
  1684  1047 5946.08675   26  250 16403.5243 5133.62810  68.7%   181   40s
  1974  1128 7430.91434   37  183 16403.5243 5133.62810  68.7%   191   45s
H 2485  1253                    16403.524297 5133.62810  68.7%   194   49s
  2537  1288 6437.59255   20  292 16403.5243 5133.62810  68.7%   195   50s
  3171  1391 12788.4759   31  247 16403.5243 5707.89908  65.2%   197   55s
  3731  1567     cutoff   29      16403.5243 5912.13645  64.0%   202   60s
H 3915  1500                    16403.524284 5936.48380  63.8%   206   62s
  4238  1625 15925.1481   29  132 16403.5243 6181.51878  62.3%   213   65s
  4635  1816 13721.9076   34  227 16403.5243 6329.60823  61.4%   219   70s
  4921  1973 12073.0263   27  246 16403.5243 6481.78741  60.5%   223   75s
H 4924  1973                    16403.524266 6481.78741  60.5%   222   75s
H 4996  1973                    16403.524252 6481.78741  60.5%   224   75s
H 5085  1981                    16403.524218 6481.78741  60.5%   225   76s
H 5093  1981                    16403.524203 6536.95583  60.1%   225   76s
H 5151  2051                    16403.524201 6548.42423  60.1%   227   78s
H 5322  2086                    16403.524157 6550.12403  60.1%   231   79s
H 5408  2086                    16403.524148 6550.12403  60.1%   231   79s
  5409  2180 15102.6718   22  393 16403.5241 6633.45060  59.6%   231   81s
H 5630  2216                    16403.524131 6633.45060  59.6%   233   83s
H 5700  2210                    16320.074675 6633.45060  59.4%   234   83s
H 5769  2270                    16265.399568 6751.64795  58.5%   236   84s
  5877  2357 8566.99998   41  172 16265.3996 6788.90418  58.3%   237   86s
  6171  2505 10485.7343   31  178 16265.3996 6928.79627  57.4%   241   90s
H 6548  2607                    16265.399540 7074.36271  56.5%   241   92s
H 6771  2691                    16265.399522 7206.98333  55.7%   242   94s
  6848  2782 9512.31074   36  190 16265.3995 7216.17296  55.6%   243   96s
  7439  3129 11905.1467   69  133 16265.3995 7427.81777  54.3%   243  101s
  8096  3467 14796.0865   47  171 16265.3995 7640.83057  53.0%   243  106s
  8847  3841 15925.0756   82  172 16265.3995 7790.81816  52.1%   242  111s
H 8987  3841                    16265.399360 7843.76620  51.8%   241  111s
  9462  3971 15648.1280   27  173 16265.3994 7996.64763  50.8%   242  132s
H 9759  3971                    16265.399355 8017.99594  50.7%   244  132s
  9794  4159 13956.6795   40  137 16265.3994 8079.15858  50.3%   244  136s
H10257  4181                    16265.399348 8146.29873  49.9%   244  139s
H10314  4181                    16265.399325 8189.53473  49.7%   243  139s
 10402  4364 12161.2472   43  140 16265.3993 8213.83056  49.5%   243  142s
H10629  4364                    16265.399294 8224.77960  49.4%   243  142s
 10907  4490 15382.1160   42  195 16265.3993 8249.64269  49.3%   244  145s
 11667  4799     cutoff   37      16265.3993 8431.56400  48.2%   244  152s
H12271  4975                    16265.399287 8545.95755  47.5%   244  156s
 12728  5116 14805.1848   33  199 16265.3993 8659.89985  46.8%   243  160s
 13482  5461 9449.79529   39  183 16265.3993 8775.02858  46.1%   243  168s
 14088  5600 12775.0089   49  157 16265.3993 8862.34850  45.5%   242  173s
 14650  5829     cutoff   31      16265.3993 8977.20181  44.8%   242  179s
 15392  6050 15589.9351   38  133 16265.3993 9111.92676  44.0%   241  183s
 16044  6301     cutoff   30      16265.3993 9197.09679  43.5%   241  187s
 16709  6336     cutoff   32      16265.3993 9284.24523  42.9%   240  202s
 16805  6578 15881.4301   40   93 16265.3993 9299.30495  42.8%   240  207s
 17559  6739 14202.2732   32  174 16265.3993 9367.69827  42.4%   240  211s
 18189  6912 11536.3438   60  140 16265.3993 9454.64159  41.9%   240  217s
 18833  7165 11607.2751   37  195 16265.3993 9547.45918  41.3%   240  222s
 19688  7218 10463.8150   23  215 16265.3993 9661.05263  40.6%   239  228s
 19894  7430 14383.2953   29  286 16265.3993 9700.43778  40.4%   238  234s
 20809  7702     cutoff   33      16265.3993 9767.61975  39.9%   238  239s
 21769  7981     cutoff   36      16265.3993 9863.93203  39.4%   238  245s
 22773  8168 14848.3101   37  205 16265.3993 9939.22640  38.9%   237  250s
 23651  8312 10260.7946   32  262 16265.3993 10039.8212  38.3%   237  256s
 24008  8607 13742.5118   30  338 16265.3993 10042.9903  38.3%   236  261s
 24978  8895 11410.3250   34  277 16265.3993 10165.0069  37.5%   236  267s
 25991  8944 12391.7154   28  194 16265.3993 10238.7663  37.1%   235  275s
H26006  8944                    16265.399285 10238.7663  37.1%   235  275s
H26009  8944                    16265.399281 10247.0449  37.0%   235  275s
H26023  8944                    16265.399179 10247.4146  37.0%   235  275s
 26145  9101     cutoff   39      16265.3992 10260.6933  36.9%   235  280s
 26882  9298 12908.1776   31  172 16265.3992 10313.4583  36.6%   235  286s
 27746  9519 11811.8469   24  267 16265.3992 10406.4679  36.0%   234  292s
 28574  9749 14031.0354   32  236 16265.3992 10452.9440  35.7%   235  297s
 29515 10021 16219.3824   30  215 16265.3992 10510.3985  35.4%   234  366s
 30758 10127 12082.8638   37  202 16265.3992 10571.3201  35.0%   233  370s
 31534 10379 15014.2418   88   70 16265.3992 10652.9914  34.5%   238  380s
 32112 10463 15393.2837   48  139 16265.3992 10677.7296  34.4%   240  385s
 32557 10546 15734.8321   37  257 16265.3992 10709.3618  34.2%   243  391s
 32985 10603 15189.1443   33  184 16265.3992 10747.8844  33.9%   247  398s
 33310 10736 14212.1987   25  271 16265.3992 10756.6034  33.9%   249  406s
 33932 10840 14325.0567   44  191 16265.3992 10820.7750  33.5%   252  413s
 34551 10849 13618.9087   32  257 16265.3992 10855.3175  33.3%   256  422s
H34795 10998                    16265.398995 10885.6959  33.1%   258  431s
 35320 11127     cutoff   29      16265.3990 10914.3803  32.9%   262  440s
 35985 11218 infeasible   46      16265.3990 10933.4725  32.8%   266  465s
 36472 11323 11698.6435   32  370 16265.3990 10969.2893  32.6%   268  476s
 37060 11410 15888.0619   29  244 16265.3990 11023.9864  32.2%   272  485s
 37681 11565 13303.9384   37  217 16265.3990 11049.0185  32.1%   276  494s
 38319 11644 12493.8377   84  318 16265.3990 11085.8846  31.8%   280  503s
 38780 11773 15233.7528   61  203 16265.3990 11107.2597  31.7%   282  512s
 39462 11956 13168.1359   35  323 16265.3990 11148.2459  31.5%   286  521s
 40108 12120 12360.9242   43  164 16265.3990 11170.6058  31.3%   289  531s
 40823 12167 15860.9538   38  172 16265.3990 11224.3156  31.0%   293  542s
 41181 12307 14683.4847   68  118 16265.3990 11241.6083  30.9%   294  552s
 41954 12480 13471.3550   44  349 16265.3990 11279.5997  30.7%   298  562s
 42698 12567     cutoff   34      16265.3990 11305.6808  30.5%   301  573s
 43175 12722 13704.6962   44  211 16265.3990 11345.1290  30.2%   304  584s
 44033 12779 15279.2714   47  151 16265.3990 11387.3869  30.0%   307  592s
 44346 12872     cutoff   71      16265.3990 11407.2477  29.9%   308  600s

Cutting planes:
  Gomory: 82
  Cover: 619
  Implied bound: 15
  Clique: 1
  MIR: 250
  Flow cover: 932
  GUB cover: 104
  Inf proof: 22
  Zero half: 163
  RLT: 565
  Relax-and-lift: 81

Explored 44748 nodes (13931369 simplex iterations) in 600.01 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 16265.4 16265.4 16265.4 ... 16403.5

Time limit reached
Best objective 1.626539899516e+04, best bound 1.143317660204e+04, gap 29.7086%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	4.000000e+00	8.500000e+00
1	1	Borås	False	False	0.7	1.610000e+01	1.680000e+01
2	2	Eskilstuna	True	False	1.1	7.000001e+00	8.100001e+00
3	3	Falun	False	False	2.3	2.202682e-13	2.300000e+00
4	4	Gävle	False	False	1.0	3.130000e+01	3.230000e+01
5	5	Göteborg	False	False	5.8	3.118392e-07	5.800000e+00
6	6	Halmstad	False	False	3.4	6.099999e+00	9.499999e+00
7	7	Haparanda	False	False	4.6	0.000000e+00	4.600000e+00
8	8	Helsingborg	False	False	2.2	3.899999e+00	6.099999e+00
9	9	Hudiksvall	False	False	3.9	2.740000e+01	3.130000e+01
10	10	Jönköping	False	False	1.2	1.680000e+01	1.800000e+01
11	11	Kalmar	False	False	4.0	0.000000e+00	4.000000e+00
12	12	Karlskrona	False	False	1.6	6.283449e-07	1.600001e+00
13	13	Karlstad	False	False	0.9	0.000000e+00	9.000000e-01
14	14	Kiruna	False	False	4.0	0.000000e+00	4.000000e+00
15	15	Kristianstad	False	False	3.0	2.131628e-13	3.000000e+00
16	16	Lidköping	False	False	1.2	7.600000e+00	8.800000e+00
17	17	Linköping	True	False	4.1	1.210000e+01	1.620000e+01
18	18	Luleå	False	False	2.0	1.310000e+01	1.510000e+01
19	19	Malmö	False	False	2.6	1.299999e+00	3.899999e+00
20	20	Motala	True	False	2.4	5.450000e+01	5.690000e+01
21	21	Norrköping	True	False	1.7	4.600000e+00	6.300000e+00
22	22	Nyköping	False	False	4.6	0.000000e+00	4.600000e+00
23	23	Sandviken	False	False	4.5	0.000000e+00	4.500000e+00
24	24	Skellefteå	False	False	4.4	1.510000e+01	1.950000e+01
25	25	Skövde	False	False	2.5	8.800000e+00	1.130000e+01
26	26	Stockholm	False	False	7.0	2.877698e-13	7.000000e+00
27	27	Sundsvall	False	False	0.7	2.670000e+01	2.740000e+01
28	28	Trelleborg	False	False	1.3	4.121148e-13	1.300000e+00
29	29	Uddevalla	False	False	4.0	7.069900e-13	4.000000e+00
30	30	Umeå	False	False	3.2	1.950000e+01	2.270000e+01
31	31	Uppsala	False	False	1.9	3.230000e+01	3.420000e+01
32	32	Varberg	False	False	0.8	9.499999e+00	1.030000e+01
33	33	Vetlanda	False	False	2.1	6.900000e+00	9.000000e+00
34	34	Vänersborg	False	False	3.6	4.000000e+00	7.600000e+00
35	35	Västervik	False	False	1.8	4.000000e+00	5.800000e+00
36	36	Västerås	True	False	1.4	4.910000e+01	5.050000e+01
37	37	Växjö	False	False	2.3	4.600000e+00	6.900000e+00
38	38	Örebro	True	True	4.1	1.083000e+02	4.263256e-13
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	2.580000e+01
40	40	Östersund	False	False	0.9	8.135714e-13	9.000000e-01

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.499999	731.139108	111
1	Eskilstuna	Västerås	CORE	8.100001	430.000000	43
2	Gävle	Hudiksvall	SUB	-31.299999	1278.724508	118
3	Norrköping	Nyköping	SUB	-4.600000	74.253949	58
4	Linköping	Norrköping	CORE	-6.299999	440.000000	44
5	Motala	Örebro	CORE	56.900000	920.000000	92
6	Karlskrona	Växjö	SUB	1.600000	62.121900	102
7	Boden	Kiruna	SUB	-4.000000	637.912972	291
8	Sundsvall	Östersund	SUB	-0.900000	70.870188	166
9	Eskilstuna	Stockholm	SUB	-7.000000	234.688115	101
10	Halmstad	Helsingborg	SUB	-6.099999	145.447323	79
11	Kalmar	Västervik	SUB	4.000000	217.291833	139
12	Umeå	Örnsköldsvik	SUB	22.699999	849.479893	111
13	Lidköping	Skövde	SUB	8.800000	105.450189	49
14	Karlstad	Örebro	SUB	0.899999	29.229953	77
15	Halmstad	Varberg	SUB	9.499999	167.432207	65
16	Lidköping	Vänersborg	SUB	-7.600000	121.696732	60
17	Motala	Skövde	SUB	-11.299999	468.037910	118
18	Uddevalla	Vänersborg	SUB	4.000000	22.172756	21
19	Hudiksvall	Sundsvall	SUB	-27.399999	641.652281	81
20	Linköping	Motala	CORE	16.200001	510.000000	51
21	Sandviken	Västerås	SUB	4.500000	174.172927	110
22	Borås	Göteborg	SUB	-5.800000	119.727508	71
23	Falun	Västerås	SUB	2.300000	115.327746	128
24	Linköping	Västervik	SUB	-5.800000	185.220774	97
25	Sundsvall	Örnsköldsvik	SUB	-25.799999	1177.683824	127
26	Jönköping	Motala	SUB	18.000000	648.863591	108
27	Vetlanda	Växjö	SUB	-6.900000	143.305416	72
28	Borås	Varberg	SUB	-10.299999	260.758752	84
29	Haparanda	Luleå	SUB	4.600000	210.858567	124
30	Luleå	Skellefteå	SUB	15.099999	742.410209	133
31	Kristianstad	Växjö	SUB	2.999999	134.707632	120
32	Motala	Vetlanda	SUB	-9.000000	456.419490	135
33	Uppsala	Västerås	SUB	34.199999	755.020104	78
34	Gävle	Uppsala	SUB	32.299999	484.711092	60
35	Helsingborg	Malmö	SUB	-3.899999	67.318051	60
36	Västerås	Örebro	CORE	50.500001	930.000000	93
37	Borås	Jönköping	SUB	16.799999	404.484584	82
38	Boden	Luleå	SUB	8.500000	58.656838	32
39	Malmö	Trelleborg	SUB	-1.299999	18.150073	34
40	Eskilstuna	Norrköping	CORE	-0.000001	1020.000000	102

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  4250.000
subnet = 12015.399
------------------
total  = 16265.399

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))